Classroom Adoptions

  • University of Pittsburgh: 1) CS2001, 2) CS2610, 3) CS0401, 4) CS1635, 5) PHYS0174, 6) PHYS0175, 7) PSY0422.
  • Purdue University: 1) ENGR131, 2) ENGR132, 3) MSE330, 4) MATH16020, 5) MET230, 6) CHEM126, 7) ME354, 8) ME 492, 9) IET33520, 10) EAPS111.
  • Bogazici University: 1) IE256, 2) IE312.
  • Thiel College: 1) MATH125.
  • Ivy Tech Community College: 1) MATH043, 2) MATH123, 3) MATH137, 4) MATH201.
  • University of Florida: 1) CGS2531, 2) COP2271, 3) COP2274.

Publications

  1. Anwar, S., Butt, A. A., & Menekse, M. (2025). Utilizing an NLP-supported mobile reflection application to explore academic engagement, application engagement, and performance in engineering and physics courses. International Journal of STEM Education, 12(1), 41. https://doi.org/10.1186/s40594-025-00551-5
  2. Elaraby, M., & Litman, D. (2025). ARC: Argument Representation and Coverage Analysis for Zero-Shot Long Document Summarization with Instruction Following LLMs (No. arXiv:2505.23654). arXiv. https://doi.org/10.48550/arXiv.2505.23654
  3. Elaraby, M., & Litman, D. (2025). Lessons Learned in Assessing Student Reflections with LLMs. In E. Kochmar, B. Alhafni, M. Bexte, J. Burstein, A. Horbach, R. Laarmann-Quante, A. Tack, V. Yaneva, & Z. Yuan (Eds.), Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025) (pp. 672–686). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.bea-1.48
  4. Zhong, Y., & Litman, D. (2025). From Information to Insight: Leveraging LLMs for Open Aspect-Based Educational Summarization. In W. Che, J. Nabende, E. Shutova, & M. T. Pilehvar (Eds.), Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 1914–1947). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.acl-long.95
  5. Kim, J., Menekse, M., Putra, A. S., Babar, E., Butt, A. A., & Anwar, S. (2025, June 22). Understanding Students’ Confusion and Interest in an Introductory Physics Course Through Qualitative Analysis of Self-Reflections. 2025 ASEE Annual Conference & Exposition. https://doi.org/10.18260/1-2–57741
  6. Satya Putra, A., Menekse, M., & Butt, A. A. (2025). WIP: Leveraging LLM for Sentiment Analysis of Student Reflection Texts from a Large Undergraduate Course. Proceedings of the Twelfth ACM Conference on Learning @ Scale, 266–270. https://doi.org/10.1145/3698205.3733937
  7. Menekse, M., Putra, A. S., Kim, J., Butt, A. A., McDaniel, M., Davidesco, I., Cadieux, M., Kim, J., & Litman, D. (2025). Enhancing Student Reflections with Natural Language Processing based Scaffolding: A Quasi-Experimental Study in a Large Lecture Course. Computers and Education: Artificial Intelligence, 100397. https://doi.org/10.1016/j.caeai.2025.100397
  8. Kim, J., Satya Putra, A., Anwar, S., Butt, A. A., Magooda, A., Litman, D., & Menekse, M. (2024). The Role of Reflection-Informed Learning and Instruction in an Introductory Physics Course for Engineering and Science Students. Proceedings of the 52nd Annual Conference of SEFI, Lausanne, Switzerland. https://doi.org/10.5281/zenodo.14254784
  9. Zhong, Y., Elaraby, M., Litman, D., Butt, A. A., & Menekse, M. (2024). ReflectSumm: A Benchmark for Course Reflection Summarization. In N. Calzolari, M.-Y. Kan, V. Hoste, A. Lenci, S. Sakti, & N. Xue (Eds.), Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) (pp. 13819–13846). ELRA and ICCL. https://aclanthology.org/2024.lrec-main.1207/
  10. Satya Putra, A., Butt, A. A., Jannini, A., & Menekse, M. (2024, April 12). Comparing Average Reflection Count and Specificity Scores Between Scaffolded and Nonscaffolded Students. AERA 2024 Annual Meeting, Philadelphia, USA. https://doi.org/10.3102/2107388
  11. Butt, A. A., Demirci Ph.D., F., & Menekse, M. (2023). Investigating the Link Between Students’ Written and Survey-Based Reflections in an Engineering Class. 2023 IEEE Frontiers in Education Conference (FIE), 1–4. https://doi.org/10.1109/FIE58773.2023.10343039
  12. Butt, A. A., Anwar, S., & Menekse, M. (2023). Work in progress: Uncovering engineering students’ sentiments from weekly reflections using natural language processing. In Proceedings of the 2023 ASEE Annual Conference & Exposition, Baltimore, MD, June 25-28, 2023. https://peer.asee.org/43210
  13. Menekse, M. (2023). Envisioning the future of learning and teaching engineering in the artificial intelligence era: Opportunities and challenges. Journal of Engineering Education, 112: 578-582. https://doi.org/10.1002/jee.20539
  14. Butt, A., Anwar, S., & Menekse, M. (2023). How Do NLP-Supported Scaffolding Techniques Support Students’ Written Reflections?, In proceedings of the 2023 17th International Technology, Education and Development Conference, Valencia, Spain, p. 7450, 6-8 March, 2023. http://dx.doi.org/10.21125/inted.2023.2036
  15. Butt, A., & Menekse, M. (2023). The Impact of Reminder Nudge on STEM Students’ Application Engagement. In proceedings of the 2023 17th International Technology, Education and Development Conference, Valencia, Spain, p. 7992, 6-8 March, 2023. https://doi.org/10.21125/inted.2023.2170
  16. Magooda, A., Litman, D., Butt, A. A., & Menekse, M. (2022). Improving the Quality of Students’ Written Reflections using Natural Language Processing: Model Design and Classroom Evaluation. In Proceedings of the 23rd International Conference on Artificial Intelligence in Education, Durham, UK, pp. 519-525, July 2022. https://doi.org/10.1007/978-3-031-11644-5_43
  17. Anwar, S., Butt, A. A., and Menekse, M. (2022) Exploring Relationships Between Academic Engagement, Application Engagement, and Academic Performance in a First-Year Engineering Course. In Proceedings of the 2022 IEEE Frontiers in Education Conference (FIE), Uppsala, Sweden, 2022, pp. 1-5, doi: 10.1109/FIE56618.2022.9962530.
  18. Butt, A. A., Anwar, S., Magooda, A., & Menekse, M. (2022). Comparative analysis of the rule-based and machine learning approach for assessing student reflections. In Chinn, C., Tan, E., Chan, C., & Kali, Y. (Eds.), In Proceedings of the 16th International Conference of the Learning Sciences – ICLS 2022 (pp. 1577-1580). International Society of the Learning Sciences. (pdf)
  19. Butt, A. A., & Anwar, S., & Menekse, M. (2022, August). WIP: Role of digital nudging strategies on STEM students’ application engagement. In Proceedings of the 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. https://peer.asee.org/41039.
  20. Magooda, A., Litman, D. & Elaraby M. (2021). Exploring Multitask Learning for Low-Resource Abstractive Summarization. Findings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), Punta Cana, Dominican Republic, November 2021. (link)
  21. Magooda, A. & Litman, D. (2021). Mitigating Data Scarceness through Data Synthesis, Augmentation and Curriculum for Abstractive Summarization. Findings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), Punta Cana, Dominican Republic, November 2021. (link)
  22. Magooda, A., & Litman, D. (2020). Abstractive Summarization for Low Resource Data using Domain Transfer and Data Synthesis. In Proceedings of the 33rd International FLAIRS Conference, North Miami Beach, Florida, May 2020. (pdf)
  23. Menekse, M. (2020). The reflection-informed learning and instruction to improve students’ academic success in undergraduate classrooms. The Journal of Experimental Education, 88(2), 183-199. https://doi.org/10.1080/00220973.2019.1620159
  24. Menekse, M., Anwar, S., & Akdemir, Z. G. (2020). How Do Different Reflection Prompts Affect Engineering Students’ Academic Performance and Engagement? The Journal of Experimental Education, 90(2), 261–279. https://doi.org/10.1080/00220973.2020.1786346
  25. Anwar, S., & Menekse, M. (2020). A systematic review of observation protocols used in postsecondary STEM classrooms. Review of Education, 9(1), 81-120. https://doi.org/10.1002/rev3.3235
  26. Luo, W., Liu, F., Liu, Z., & Litman, D. (2018). A Novel ILP Framework for Summarizing Content with High Lexical Variety. Natural Language Engineering, Volume 24, Issue 6, pp. 887-920. http://dx.doi.org/10.1017/S1351324918000323
  27. Heo, D., Anwar, S., & Menekse, M. (2018). The relationship between engineering students’ achievement goals, reflection behaviors, and learning outcomes. International Journal of Engineering Education, 34(5), 1634-1643 (pdf)
  28. Menekse, M., Anwar, S., & Purzer, S. (2018). Self-Efficacy and Mobile Learning Technologies: A Case Study of CourseMIRROR. In C. B. Hodges (Ed.), Self-Efficacy in Instructional Technology Contexts, Springer Nature Switzerland AG 2018. (pdf)
  29. Anwar, S., Menekse, M., Heo, D., & Kim, D. (2018). Work-in-Progress: Students’ reflection quality and effective team membership. In Proceedings of the 2018 ASEE Annual Conference, Salt Lake City, Utah. (pdf)
  30. Heo, D., Anwar, S., & Menekse, M. (2017). How do engineering students’ achievement goals influence their reflection behaviors and learning outcomes? In Proceedings of the 2017 ASEE Annual Conference, Columbus, Ohio. (pdf)
  31. Fan, X., Luo, W., Menekse, M., Litman, D., & Wang, J. (2017). Scaling reflection prompts in large classrooms via mobile interfaces and natural language processing. In Proceedings of 22nd ACM Conference on Intelligent User Interfaces (IUI 2017), Limassol, Cyprus. (pdf)
  32. Luo, W., Liu, F., & Litman, D. (2016). An improved phrase-based approach to annotating and summarizing student course responses. In Proceedings of the 26th International Conference on Computational Linguistics (COLING), pp. 53-63, Osaka, Japan. (pdf)
  33. Luo, W., & Litman, D. J. (2016). Determining the quality of a student reflective response. In Proceedings 29th International FLAIRS Conference, pp. 226-231, Key Largo, FL. (Best Student Paper Award Nominee) (pdf)
  34. Luo, W., Liu, F., Liu, Z., & Litman, D. (2016). Automatic summarization of student course feedback. In Proceedings Conference of the North American Chapter of the Association for Computational Linguistics – Human Language Technologies (NAACL HLT), pp. 80-85, San Diego, CA. (short paper) (pdf)
  35. Fan, X., Luo, W., Menekse, M., Litman, D., & Wang, J. (2015). CourseMIRROR: Enhancing large classroom instructor-student interactions via mobile interfaces and natural language processing. Works-In-Progress, In Proceedings of ACM Conference on Human Factors in Computing Systems (CHI 2015), 1473-1478, Seoul, Korea. (extended abstract) (pdf)
  36. Luo, W., Fan, X., Menekse, M., Wang, J., & Litman, D. J. (2015). Enhancing instructor-student and student-student interactions with mobile interfaces and summarization. In Proceedings NAACL HLT Companion, 16-20, Denver, CO. (demo) (pdf)
  37. Luo, W., & Litman, D. J. (2015). Summarizing student responses to reflection prompts. In Proceedings of Empirical Methods in Natural Language Processing (EMNLP ) pp. 1955–1960, Lisbon, Portugal (short paper). (pdf)

Dissertations

  1. Jannini, A. V. S. (2024). Developing Motivational Profiles of First-Year Engineering Students Using Latent Profile Analysis [Thesis, Purdue University Graduate School]. https://doi.org/10.25394/PGS.26337082.v1
  2. Butt, A. A. (2023). The Role of Digital Nudges in Engineering Students’ Engagement with an Educational Mobile Application [Thesis, Purdue University Graduate School]. https://doi.org/10.25394/PGS.23730531.v1
  3. Magooda, A. (2022, June 6). Techniques to Enhance Abstractive Summarization Model Training for Low Resource Domains [University of Pittsburgh ETD]. University of Pittsburgh. https://d-scholarship.pitt.edu/42259/
  4. Anwar, S. (2020). Role of Different Instructional Strategies on Engineering Students’ Academic Performance and Motivational Constructs [Thesis, Purdue University Graduate School]. https://doi.org/10.25394/PGS.12706799.v1
  5. Fan, X. (2017, June 25). Scalable Teaching and Learning via Intelligent User Interfaces [University of Pittsburgh ETD]. University of Pittsburgh. https://d-scholarship.pitt.edu/31133/
  6. Luo, W. (2017, September 27). Automatic Summarization for Student Reflective Responses [University of Pittsburgh ETD]. University of Pittsburgh. https://d-scholarship.pitt.edu/31455/